Bleed-out identification method and system for crystallizer of continuous casting machine

Abstract

The invention provides a bleed-out identification method and system for a crystallizer of a continuous casting machine, and the method comprises the steps: obtaining the thermocouple data of the crystallizer, the pulling speed data of the continuous casting machine and the liquid level data of the crystallizer of the continuous casting machine in a set time interval before a bonding characteristic change moment; the standard deviation of the continuous casting machine pulling speed data and the continuous casting machine crystallizer liquid level data in the set time interval is determined; when the standard deviation of the casting speed data of the continuous casting machine and the crystallizer liquid level data of the continuous casting machine is smaller than a first threshold value, the crystallizer of the continuous casting machine is judged to be in a first state; when the standard deviation of the casting speed data of the continuous casting machine and the liquid level data of the crystallizer of the continuous casting machine is larger than the first threshold value and smaller than a second threshold value, the crystallizer of the continuous casting machine is judged to be in a second state; when the standard deviation of the continuous casting machine pulling speed data and the continuous casting machine crystallizer liquid level data is larger than a second threshold value, it is judged that the continuous casting machine crystallizer is in a third state. According to the method, the technical effect of greatly reducing the false alarm rate in an unsteady continuous casting production scene is finally achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of continuous casting machines, and more specifically, to a method and system for identifying steel leakage in a continuous casting machine crystallizer. Background Art

[0002] During the continuous casting process, if the solidified billet shell formed in the crystallizer breaks for some reason, and the rupture cannot be resolidified and healed before the billet is pulled out of the crystallizer, the unsolidified molten steel in the crystallizer and the billet will suddenly leak out. This accident is called breakout. Breakout is one of the most serious accidents in continuous casting production. Breakout will cause direct economic losses and destroy the orderliness of production organization. The monitoring of crystallizer breakout events is a very important part of steel production. It can be achieved through the following monitoring technologies: temperature monitoring based on thermocouples, friction monitoring, and monitoring based on heat flow analysis. Among them, the temperature monitoring method based on thermocouples is currently the most commonly used and most effective method for predicting breakouts because of its high accuracy and reliability. Thermocouple-based temperature monitoring monitors the temperature changes in the crystallizer in real time by embedding a certain number of thermocouples in the copper plate of the crystallizer. When breakout occurs in the crystallizer, the molten steel is in direct contact with the copper plate, causing the temperature of the thermocouples at the corresponding position to rise sharply. Breakouts are predicted by monitoring this temperature change. In the steady-state production process, the accuracy of steel leakage prediction based on the temperature monitoring method of thermocouple is higher.

[0003] However, because the temperature change characteristics in the crystallizer during the non-steady-state production process are somewhat similar to the temperature change characteristics during steel leakage, it is difficult for the system to accurately distinguish them; as a result, under non-steady-state production conditions, the traditional steel leakage prediction system has a high false alarm rate.

[0004] Therefore, there is an urgent need for a continuous casting machine mold leak identification method suitable for non-steady-state production. Summary of the invention

[0005] In view of the above problems, an object of the present invention is to provide a method and system for identifying steel leakage in a continuous casting machine crystallizer, so as to solve at least one problem existing in the prior art.

[0006] According to one aspect of the present invention, a method for identifying steel leakage in a continuous casting machine crystallizer is provided, which is applied to electronic equipment and comprises:

[0007] Acquiring mold thermocouple data, and determining a bonding characteristic change time according to the mold thermocouple data;

[0008] Acquire the continuous casting machine casting speed data and the continuous casting machine crystallizer liquid level data within a set time interval before the bonding characteristic changes;

[0009] Determine the standard deviation of the continuous casting machine casting speed data and the continuous casting machine crystallizer liquid level data within a set time interval according to the acquired continuous casting machine casting speed data and the continuous casting machine crystallizer liquid level data;

[0010] The state of the crystallizer of the continuous casting machine is determined according to the standard deviation; wherein, when the standard deviation of the continuous casting machine pulling speed data and the continuous casting machine crystallizer liquid level data is less than a first threshold value, it is determined that the crystallizer of the continuous casting machine is in the first state;

[0011] When the standard deviation of the continuous casting machine casting speed data and the continuous casting machine crystallizer liquid level data is greater than a first threshold value and less than a second threshold value, it is determined that the crystallizer of the continuous casting machine is in the second state;

[0012] When the standard deviation of the continuous casting machine casting speed data and the continuous casting machine crystallizer liquid level data is greater than a second threshold, it is determined that the crystallizer of the continuous casting machine is in the third state.

[0013] In addition, the optional technical solution also includes:

[0014] The first state is a stable operation state of the continuous casting machine;

[0015] The second state is a second stable operation state of the continuous casting machine;

[0016] The third state is an abnormal operation state of the continuous casting machine.

[0017] In addition, the optional technical solution also includes:

[0018] When it is determined that the crystallizer of the continuous casting machine is in the first state, the initial temperature and the maximum temperature of the upper thermocouple when the temperature rise curve appears, the initial temperature and the maximum temperature of the lower thermocouple when the temperature rise curve appears, the high temperature point moment of the upper thermocouple, the high temperature point moment of the lower thermocouple, the actual physical distance between the upper thermocouple and the lower thermocouple, and the continuous casting machine casting speed data are obtained;

[0019] The minimum temperature rise of the upper thermocouple is obtained according to the initial temperature and the maximum temperature of the upper thermocouple when the temperature rise curve appears, and the minimum temperature rise of the lower thermocouple is obtained according to the initial temperature and the maximum temperature of the lower thermocouple when the temperature rise curve appears; the speed of the high temperature point moving between the two thermocouples at the moment of the high temperature point is obtained according to the high temperature point moment of the upper thermocouple, the high temperature point moment of the lower thermocouple, and the actual physical distance between the upper thermocouple and the lower thermocouple; and then the ratio of the speed of the high temperature point moving between the two thermocouples at the moment of the high temperature point to the casting speed is determined according to the speed of the high temperature point moving between the two thermocouples at the moment of the high temperature point and the casting speed data of the continuous casting machine;

[0020] The crystallizer state is monitored according to the first rule; if the minimum temperature rise of the upper thermocouple or the minimum temperature rise of the lower thermocouple and the ratio of the speed of the high temperature point moving between the two thermocouples to the pulling speed meet the first rule, a steel leakage alarm signal is issued.

[0021] In addition, the optional technical solution also includes:

[0022] When it is determined that the crystallizer of the continuous casting machine is in the second state, the initial temperature and the maximum temperature of the upper thermocouple when the temperature rise curve appears, the initial temperature and the maximum temperature of the lower thermocouple when the temperature rise curve appears, the high temperature point moment of the upper thermocouple, the high temperature point moment of the lower thermocouple, the actual physical distance between the upper thermocouple and the lower thermocouple, and the continuous casting machine casting speed data are obtained;

[0023] The minimum temperature rise of the upper thermocouple is obtained according to the initial temperature and the maximum temperature of the upper thermocouple when the temperature rise curve appears, and the minimum temperature rise of the lower thermocouple is obtained according to the initial temperature and the maximum temperature of the lower thermocouple when the temperature rise curve appears; the speed of the high temperature point moving between the two thermocouples at the moment of the high temperature point is obtained according to the high temperature point moment of the upper thermocouple, the high temperature point moment of the lower thermocouple, and the actual physical distance between the upper thermocouple and the lower thermocouple; and then the ratio of the speed of the high temperature point moving between the two thermocouples at the moment of the high temperature point to the casting speed is determined according to the speed of the high temperature point moving between the two thermocouples at the moment of the high temperature point and the casting speed data of the continuous casting machine;

[0024] The crystallizer state is monitored according to the second rule; if the minimum temperature rise of the upper thermocouple or the minimum temperature rise of the lower thermocouple and the ratio of the speed of the high temperature point moving between the two thermocouples to the pulling speed meet the second rule, a steel leakage alarm signal is issued.

[0025] In addition, the optional technical solution also includes:

[0026] When it is determined that the crystallizer of the continuous casting machine is in the third state, the continuous casting machine casting speed data, the continuous casting machine crystallizer liquid level data and the crystallizer vibration data are obtained;

[0027] The abnormal state type of the continuous casting machine is determined according to the acquired continuous casting machine casting speed data, continuous casting machine crystallizer liquid level data and crystallizer vibration data.

[0028] In addition, the optional technical solution also includes:

[0029] The abnormal state types of the continuous casting machine include any one of the casting machine water inlet replacement state, tundish replacement state, abnormal accident state, casting start and casting speed increase state, casting stop state, casting speed increase state and casting speed reduction state.

[0030] In addition, the optional technical solution also includes:

[0031] After determining the abnormal state type of the continuous casting machine according to the acquired continuous casting machine casting speed data, continuous casting machine crystallizer liquid level data and crystallizer vibration data, the method further includes:

[0032] According to the abnormal state type of the continuous casting machine, steel leakage monitoring is performed according to the determination rules corresponding to the abnormal state type of the continuous casting machine.

[0033] On the other hand, the present invention also provides a continuous casting machine crystallizer breakout identification system, which uses the continuous casting machine crystallizer breakout identification method as described above to identify breakout; the system comprises:

[0034] A data acquisition unit is used to acquire mold thermocouple data, and determine the bonding characteristic change time according to the mold thermocouple data; acquire the continuous casting machine pulling speed data and the continuous casting machine mold liquid level data within a set time interval before the bonding characteristic change time;

[0035] A data processing unit is used to determine the standard deviation of the continuous casting machine casting speed data and the continuous casting machine crystallizer liquid level data within a set time interval according to the acquired continuous casting machine casting speed data and the continuous casting machine crystallizer liquid level data;

[0036] A state determination unit, used for determining the state of the crystallizer of the continuous casting machine according to the standard deviation; wherein, when the standard deviation of the continuous casting machine casting speed data and the continuous casting machine crystallizer liquid level data is less than a first threshold value, it is determined that the crystallizer of the continuous casting machine is in a first state;

[0037] When the standard deviation of the continuous casting machine casting speed data and the continuous casting machine crystallizer liquid level data is greater than a first threshold value and less than a second threshold value, it is determined that the crystallizer of the continuous casting machine is in the second state;

[0038] When the standard deviation of the continuous casting machine casting speed data and the continuous casting machine crystallizer liquid level data is greater than a second threshold, it is determined that the crystallizer of the continuous casting machine is in the third state.

[0039] The above-mentioned continuous casting machine crystallizer leakage identification method and system obtains crystallizer thermocouple data, and determines the bonding feature change time according to the crystallizer thermocouple data; obtains the continuous casting machine pulling speed data and the continuous casting machine crystallizer liquid level data within a set time interval before the bonding feature change time; determines the standard deviation of the continuous casting machine pulling speed data and the continuous casting machine crystallizer liquid level data within the set time interval according to the obtained continuous casting machine pulling speed data and the continuous casting machine crystallizer liquid level data; when the standard deviation of the continuous casting machine pulling speed data and the continuous casting machine crystallizer liquid level data is less than the first threshold, it is determined that the crystallizer of the continuous casting machine is in the first state; when the standard deviation of the continuous casting machine pulling speed data and the continuous casting machine crystallizer liquid level data is greater than the first threshold and less than the second threshold, it is determined that the crystallizer of the continuous casting machine is in the second state; when the standard deviation of the continuous casting machine pulling speed data and the continuous casting machine crystallizer liquid level data is greater than the second threshold, it is determined that the crystallizer of the continuous casting machine is in the third state. The present invention ultimately achieves the technical effect of greatly reducing the false alarm rate in the non-steady-state continuous casting production scenario.

[0040] In order to achieve the above and related purposes, one or more aspects of the present invention include the features that will be described in detail later. The following description and the accompanying drawings describe some exemplary aspects of the present invention in detail. However, these aspects indicate only some of the various ways in which the principles of the present invention can be used. In addition, the present invention is intended to include all these aspects and their equivalents. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] By referring to the following description in conjunction with the accompanying drawings, and with a more comprehensive understanding of the present invention, other objects and results of the present invention will become more clear and easy to understand. In the accompanying drawings:

[0042] Figure 1 A schematic diagram of a flow chart of a method for identifying steel leakage in a continuous casting machine crystallizer according to an embodiment of the present invention;

[0043] Figure 2 An example of liquid level data of a continuous casting machine crystallizer according to an embodiment of the present invention;

[0044] Figure 3 A schematic diagram of modules of a continuous casting machine crystallizer leakout identification system provided by one embodiment of the present invention.

[0045] Figure 4 A schematic diagram of the internal structure of an electronic device for implementing a method for identifying steel leakage in a continuous casting machine crystallizer provided by an embodiment of the present invention.

[0046] The same reference numerals throughout the drawings indicate similar or corresponding features or functions. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0048] The technical solutions in the embodiments of the present application will be described clearly and in detail below in conjunction with the accompanying drawings. In the description of the embodiments of the present application, unless otherwise specified, "and / or" in the text is only a description of the association relationship of the associated objects, indicating that three relationships may exist, for example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone.

[0049] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In addition, in the description of the embodiments of the present application, "plurality" means two or more than two.

[0050] References to "one embodiment" or "some embodiments" etc. described in this specification mean that a particular feature, structure or characteristic described in conjunction with the embodiment is included in one or more embodiments of the present application. Thus, the phrases "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear at different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0051] In order to describe the continuous casting machine crystallizer breakout identification method and system in detail, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0052] AI is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results. In other words, artificial intelligence is a comprehensive technology in computer science that attempts to understand the essence of intelligence and produce a new intelligent machine that can respond in a similar way to human intelligence. Artificial intelligence is to study the design principles and implementation methods of various intelligent machines so that machines have the functions of perception, reasoning and decision-making. Artificial intelligence technology is a comprehensive discipline that covers a wide range of fields, including both hardware-level technology and software-level technology. Basic artificial intelligence technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, and mechatronics. Artificial intelligence software technology mainly includes several major directions such as computer vision technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0053] Machine Learning (ML) is a multi-disciplinary subject that involves probability theory, statistics, approximation theory, convex analysis, algorithm complexity theory and other disciplines. It specializes in studying how computers simulate or implement human learning behavior to acquire new knowledge or skills and reorganize existing knowledge structures to continuously improve their performance. Machine learning is the core of artificial intelligence and the fundamental way to make computers intelligent. Its applications are spread across all areas of artificial intelligence. Machine learning and deep learning usually include artificial neural networks, belief networks, reinforcement learning, transfer learning, inductive learning, and self-learning.

[0054] Figure 1 A flow chart of a method for identifying steel leakage in a continuous casting machine crystallizer according to an embodiment of the present invention is shown.

[0055] like Figure 1 As shown, the continuous casting machine crystallizer leak identification method provided in this embodiment is applied to electronic equipment and mainly includes the following steps:

[0056] S110: Acquire mold thermocouple data, and determine the bonding characteristic change time according to the mold thermocouple data. S120: Acquire continuous casting machine casting speed data and continuous casting machine mold liquid level data within a set time interval before the bonding characteristic change time.

[0057] It should be noted that the intelligent control of the crystallizer, as the core component of the continuous casting machine, is crucial to the quality of the ingot. In the specific implementation process, accurate monitoring of the crystallizer thermocouple data, liquid level fluctuations in the crystallizer, dynamic changes in local heat flow, and tracking of the actual ingot shell "hot spots" can be achieved. Correctly install the thermocouple on the crystallizer copper plate to ensure good contact between the thermocouple and the copper plate, avoid oil stains, loose bolts, loose joints, etc., to ensure that the collected temperature data is accurate. Collect the temperature data of the crystallizer thermocouple in real time. Perform real-time analysis on the temperature data collected by the crystallizer thermocouple. When abnormal fluctuations in the temperature data are detected or the preset alarm threshold is reached, it can be judged as the moment when the bonding characteristics change.

[0058] In a specific embodiment, the thermocouples arranged in the copper plate of the continuous casting crystallizer are arranged in 3 to 6 rows, and the number of thermocouples arranged in each copper plate varies from 6 to 20 columns. According to the actual size of the copper plate, one column of thermocouples needs to be arranged every 133 mm. For two adjacent thermocouples, UTCT is used here to refer to the upper thermocouple, and DTCT is used to refer to the lower thermocouple. The basic temperature values ​​of the upper and lower thermocouples, the temperature rise slope, the temperature drop slope, the highest temperature point moment, the pulling speed at the highest temperature point moment and the physical distance between the upper and lower thermocouples are all objects of data collection.

[0059] In the specific implementation process, the acquired crystallizer thermocouple data can be realized through supervised learning models, unsupervised learning models, time series analysis models, etc. to determine the moment when the bonding characteristics change. A machine learning model is a mathematical structure or algorithm that can learn and infer useful information from input data, and then predict or make decisions on new data. In the field of machine learning, models are established by training on existing data. This training process involves optimizing model parameters so that it can fit the training data to the greatest extent and is expected to show good generalization ability on unknown data. For example, artificial neural network (ANN): ANN can learn the complex nonlinear relationship between input features (thermocouple data) and output labels (moment when the bonding characteristics change) through training, and has good prediction and generalization capabilities. Random forests are composed of multiple decision trees, which improve the prediction accuracy and stability by integrating the prediction results of multiple models. Clustering algorithms (such as K-means, hierarchical clustering, etc.) can be used to cluster thermocouple data, divide the data into different clusters, and then analyze the characteristics of each cluster to identify the moment when the bonding characteristics change. Long Short-Term Memory Network (LSTM): LSTM is a special recurrent neural network that can capture long-term dependencies in time series data and is suitable for modeling and predicting time series of thermocouple data. In addition, it is necessary to preprocess the data (such as denoising, normalization, etc.) and select appropriate feature engineering methods to extract key features that are helpful for model prediction. By training and testing the model and continuously optimizing the model parameters, the accurate determination of the change moment of the bonding characteristics in the crystallizer thermocouple data can be finally achieved.

[0060] S130: Determine the standard deviation of the continuous casting machine casting speed data and the continuous casting machine crystallizer liquid level data within a set time interval according to the acquired continuous casting machine casting speed data and the continuous casting machine crystallizer liquid level data.

[0061] The standard deviation is a statistic that measures the degree of dispersion of a set of data. It represents the average distance between a data point and the average value. Specifically, it is necessary to first calculate the average value, then calculate the square of the difference between each data point and the average value, and then find the average value of these square differences, i.e., the variance. Finally, take the square root of the variance to get the standard deviation. In the prior art, the liquid level fluctuation of the continuous casting machine is used as the only indicator for judging steel leakage. The present invention further improves the accuracy of steel leakage identification by using the continuous casting machine pulling speed data and the continuous casting machine crystallizer liquid level data as the prerequisite for temperature judgment of steel leakage.

[0062] The set time interval can be 40 to 150 seconds, which is determined according to the specific application scenario and is not specifically limited here.

[0063] S140: Determine the state of the crystallizer of the continuous casting machine according to the standard deviation; wherein, when the standard deviation of the continuous casting machine pulling speed data and the continuous casting machine crystallizer liquid level data is less than the first threshold value, it is determined that the crystallizer of the continuous casting machine is in the first state; when the standard deviation of the continuous casting machine pulling speed data and the continuous casting machine crystallizer liquid level data is greater than the first threshold value and less than the second threshold value, it is determined that the crystallizer of the continuous casting machine is in the second state; when the standard deviation of the continuous casting machine pulling speed data and the continuous casting machine crystallizer liquid level data is greater than the second threshold value, it is determined that the crystallizer of the continuous casting machine is in the third state. The first state is the stable operation state of the continuous casting machine; the second state is the sub-stable operation state of the continuous casting machine; and the third state is the abnormal operation state of the continuous casting machine.

[0064] Specifically, Figure 2 As shown, the crystallizer thermocouple data, continuous casting machine casting speed data, and continuous casting machine crystallizer liquid level data are placed on the same time axis for comprehensive analysis. When the temperature part of the thermocouple produces a change in the bonding characteristics, the fluctuation state of the continuous casting machine's pulling speed and liquid level within the first 60 seconds of this period is comprehensively counted. If the pulling speed and liquid level are within 5% of the comprehensive standard deviation within this period, it can be considered that the system is in the normal steady-state operation range, and real-time judgment and calculation are performed according to the standard steel leakage prediction characteristic curve related method; when the comprehensive standard deviation of the pulling speed and liquid level within this period is between 5% and 15%, it is determined that the system is in a steady but unstable operation state, and real-time judgment and calculation can be performed according to the second set of steel leakage prediction identification algorithm standards; when the comprehensive standard deviation of the pulling speed and liquid level within this period is greater than 15%, it is determined that the current casting machine state is a non-steady-state state. At this time, the production state of the continuous casting machine is comprehensively judged through relevant signals such as pulling speed, liquid level, and crystallizer vibration, which is specifically divided into the continuous casting machine water inlet change state, tundish change state, abnormal accident state, casting start and pulling speed increase state, casting stop state, pulling speed increase state, and pulling speed reduction state.

[0065] In the specific implementation process, the state of the continuous casting machine can be determined by a neural network model, a support vector machine model, etc. For example, using the BP neural network model, the pulling speed data, the liquid level data and its standard deviation are used as network inputs, and the model is trained to identify the state of the crystallizer and the signs of steel leakage. When the model determines that the crystallizer is in the first state, a steel leakage alarm is triggered. Support vector machine model: A multi-classification support vector machine model is used to classify the state information of the continuous casting machine, and the warning level is set according to the classification results. When the crystallizer is in the first state, a steel leakage alarm is issued. Specifically, a large amount of continuous casting machine operation data, including data of normal state and steel leakage accidents, is collected, and the data is annotated to form a training data set. The training data set is used to train the neural network and support vector machine models, and the model parameters are adjusted so that they can accurately identify the state of the crystallizer and the signs of steel leakage. The performance of the model is evaluated by methods such as cross-validation, and the model is optimized according to the evaluation results to reduce the omission rate and false alarm rate. The trained model is deployed to the real-time monitoring system, and the pulling speed data and liquid level data of the continuous casting machine are received in real time, and the standard deviation is calculated and the state is determined. When the model determines that the crystallizer is in the first state, the steel leakage alarm is automatically triggered to remind the operator to take corresponding measures, such as adjusting the casting speed, increasing the negative slip time, etc., to avoid steel leakage accidents.

[0066] In a specific embodiment, when it is determined that the crystallizer of the continuous casting machine is in the first state, the initial temperature and the maximum temperature of the upper thermocouple when the temperature rise curve appears, the initial temperature and the maximum temperature of the lower thermocouple when the temperature rise curve appears, the high temperature point moment of the upper thermocouple, the high temperature point moment of the lower thermocouple, the actual physical distance between the upper thermocouple and the lower thermocouple, and the continuous casting machine pulling speed data are obtained; the minimum temperature rise of the upper thermocouple is obtained according to the initial temperature and the maximum temperature of the upper thermocouple when the temperature rise curve appears, and the minimum temperature rise of the lower thermocouple is obtained according to the initial temperature and the maximum temperature of the lower thermocouple when the temperature rise curve appears ;According to the high temperature point moment of the upper thermocouple, the high temperature point moment of the lower thermocouple, and the actual physical distance between the upper thermocouple and the lower thermocouple, the speed at which the high temperature point moves between the two thermocouples is obtained; and then, according to the speed at which the high temperature point moves between the two thermocouples and the continuous casting machine pulling speed data, the ratio of the speed at which the high temperature point moves between the two thermocouples to the pulling speed is determined; the crystallizer state is monitored according to the first rule; if the minimum temperature rise of the upper thermocouple temperature or the minimum temperature rise of the lower thermocouple temperature and the ratio of the speed at which the high temperature point moves between the two thermocouples to the pulling speed meet the first rule, a steel leakage alarm signal is issued. For example, the first rule is that, since in the steady state, the corresponding minimum temperature rise of the upper and lower thermocouple temperatures is set to 4 degrees, the ratio of the speed at which the high temperature point moves between the two thermocouples to the pulling speed is between 0.1-3.0, and there is no need to make the same judgment on the left and right columns of thermocouples.

[0067] When it is determined that the crystallizer of the continuous casting machine is in the second state, the initial temperature and the maximum temperature of the upper thermocouple when the temperature rise curve appears, the initial temperature and the maximum temperature of the lower thermocouple when the temperature rise curve appears, the high temperature point moment of the upper thermocouple, the high temperature point moment of the lower thermocouple, the actual physical distance between the upper thermocouple and the lower thermocouple, and the continuous casting machine pulling speed data are obtained; the minimum temperature rise of the upper thermocouple is obtained according to the initial temperature and the maximum temperature of the upper thermocouple when the temperature rise curve appears, the minimum temperature rise of the lower thermocouple is obtained according to the initial temperature and the maximum temperature of the lower thermocouple when the temperature rise curve appears; The high temperature point moment of the upper thermocouple, the high temperature point moment of the lower thermocouple, and the actual physical distance between the upper thermocouple and the lower thermocouple are used to obtain the speed at which the high temperature point moves between the two thermocouples; then, according to the speed at which the high temperature point moves between the two thermocouples and the continuous casting machine pulling speed data, the ratio of the speed at which the high temperature point moves between the two thermocouples to the pulling speed is determined; the crystallizer state is monitored according to the second rule; if the minimum temperature rise of the upper thermocouple temperature or the minimum temperature rise of the lower thermocouple temperature and the ratio of the speed at which the high temperature point moves between the two thermocouples to the pulling speed meet the second rule, a steel leakage alarm signal is issued. For example, the second rule is that due to the relatively unstable state, the steel leakage prediction rule needs to be further limited and the requirements are more stringent. The corresponding minimum temperature rise of the upper and lower thermocouple temperatures is set to 6 degrees, and the ratio of the speed at which the high temperature point moves between the two thermocouples to the pulling speed is 0.5-2.5, and the same judgment does not need to be made for the left and right columns of thermocouples.

[0068] When it is determined that the crystallizer of the continuous casting machine is in the third state, the continuous casting machine pulling speed data, the continuous casting machine crystallizer liquid level data and the crystallizer vibration data are obtained; the abnormal state type of the continuous casting machine is determined based on the obtained continuous casting machine pulling speed data, the continuous casting machine crystallizer liquid level data and the crystallizer vibration data.

[0069] The abnormal state types of the continuous casting machine include any one of the casting machine water inlet replacement state, tundish replacement state, abnormal accident state, casting start and casting speed increase state, casting stop state, casting speed increase state and casting speed reduction state.

[0070] After determining the abnormal state type of the continuous casting machine according to the acquired continuous casting machine pulling speed data, continuous casting machine crystallizer liquid level data and crystallizer vibration data, it also includes, according to the abnormal state type of the continuous casting machine, performing steel leakage monitoring according to the determination rules corresponding to the abnormal state type of the continuous casting machine.

[0071] For example, due to the relatively unstable state, the steel leakage prediction rules need to be further limited. The third rule corresponding to the water change port working condition is set to 7 degrees for the minimum temperature rise of the upper and lower thermocouples. The ratio of the speed of the high temperature point moving between the two thermocouples to the pulling speed is 0.6-2.0, and the same judgment needs to be made for the left and right columns of thermocouples. For example, the standard for determining the water change port of the continuous casting machine is that the pulling speed of the continuous casting machine is within 8 minutes, and the rate of reduction of the pulling speed exceeds 0.3m / min. The pulling speed is reduced by more than 0.3m / min, and the reduced pulling speed is maintained for more than 30s. During this period, the standard deviation of the liquid level fluctuation is <10%. It is judged as steel leakage and a steel leakage alarm is issued.

[0072] Due to the relatively unstable state, the steel leakage prediction rules need to be further limited. The fourth rule corresponding to the tundish replacement working condition is that the minimum temperature rise of the upper and lower thermocouples is set to 8 degrees, the ratio of the speed of the high temperature point moving between the two thermocouples to the casting speed is 0.8-1.9, and the same judgment needs to be made for the left and right columns of thermocouples. For example, when the casting speed of the continuous casting machine is reduced to 0m / min, and the difference between the liquid level data and the production time is <10%, it is judged as steel leakage and a steel leakage alarm is issued.

[0073] Due to the relatively unstable state, the steel leakage prediction rules need to be further limited. The fifth rule corresponding to the accident condition is that the minimum temperature rise of the upper and lower thermocouples is set to 8 degrees, the ratio of the speed of the high temperature point moving between the two thermocouples to the pulling speed is 0.8-1.5, and the same judgment needs to be made for the left and right columns of thermocouples. For example, if the standard deviation of the crystallizer liquid level fluctuation within 5s is greater than 80% and the absolute value of the single temperature rise or temperature drop of the crystallizer water temperature is greater than 1 degree, it is judged as steel leakage and a steel leakage alarm is issued.

[0074] Due to the relatively unstable state, the steel leakage prediction rules need to be further limited. The sixth rule corresponding to the pouring condition is that the minimum temperature rise of the upper and lower thermocouples is set to 9 degrees, and the ratio of the speed of the high temperature point moving between the two thermocouples to the casting speed is 0.8-1.4, and the same judgment needs to be made for the left and right columns of thermocouples. For example, the crystallizer liquid level continues to rise and the casting speed of the continuous casting machine gradually increases from 0 to the production casting speed, which is judged as steel leakage and a steel leakage alarm is issued.

[0075] Due to the relatively unstable state, the steel leakage prediction rules need to be further limited. The seventh rule corresponding to the stop-casting condition sets the minimum temperature rise of the upper and lower thermocouples to 9 degrees, and the ratio of the speed of the high-temperature point moving between the two thermocouples to the casting speed is 0.8-1.2, and the same judgment needs to be made for the left and right columns of thermocouples. For example, if the crystallizer liquid level continues to drop and the casting speed of the continuous casting machine gradually decreases from the production casting speed to below 0.5, it is judged as steel leakage and a steel leakage alarm is issued.

[0076] Due to the relatively unstable state, the steel leakage prediction rules need to be further limited. The eighth rule corresponding to the pull speed increase condition is that the minimum temperature rise of the upper and lower thermocouples is set to 6 degrees, and the ratio of the speed of the high temperature point moving between the two thermocouples to the pull speed is 0.5-1.5, and the same judgment needs to be made for the left and right columns of thermocouples. For example, the pull speed change trend is consistent, and the absolute value of the change per minute is >0.05m / min, and the standard deviation of the crystallizer liquid level is <5%, which is judged as steel leakage and a steel leakage alarm is issued.

[0077] Due to the relatively unstable state, the leakage prediction rules need to be further limited. The ninth rule corresponding to the reduced pulling speed condition is that the minimum temperature rise of the upper and lower thermocouples is set to 8 degrees, and the ratio of the speed of the high temperature point moving between the two thermocouples to the pulling speed is 0.5-1.5, and the same judgment needs to be made for the left and right columns of thermocouples. For example, the pulling speed change trend is consistent, and the absolute value of the change per minute is >0.05m / min, and the standard deviation of the crystallizer liquid level is <5%, which is judged as leakage and a leakage alarm is issued.

[0078] The first threshold is 5%, and the second threshold is 15%. In the specific implementation process, the indicators with a difference of 5% and 15% in the entire control strategy can be freely adjusted according to the actual conditions of different production sites. No specific limitation is made here.

[0079] like Figure 3 As shown, the present invention provides a continuous casting machine crystallizer leak recognition system, which uses the continuous casting machine crystallizer leak recognition method as described above to perform voice recognition. According to the functions to be implemented, the continuous casting machine crystallizer leak recognition system 200 may include a data acquisition unit 210, a data processing unit 220, and a state determination unit 230. The unit of the present invention can also be called a module, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0080] In this embodiment, the functions of each module / unit are as follows:

[0081] The data acquisition unit 210 is used to acquire the crystallizer thermocouple data, and determine the bonding characteristic change time according to the crystallizer thermocouple data; acquire the continuous casting machine casting speed data and the continuous casting machine crystallizer liquid level data within a set time interval before the bonding characteristic change time;

[0082] The data processing unit 220 is used to determine the standard deviation of the continuous casting machine casting speed data and the continuous casting machine crystallizer liquid level data within a set time interval according to the acquired continuous casting machine casting speed data and the continuous casting machine crystallizer liquid level data;

[0083] A state determination unit 230 is used to determine the state of the crystallizer of the continuous casting machine according to the standard deviation; wherein, when the standard deviation of the continuous casting machine casting speed data and the continuous casting machine crystallizer liquid level data is less than a first threshold value, it is determined that the crystallizer of the continuous casting machine is in the first state;

[0084] When the standard deviation of the continuous casting machine casting speed data and the continuous casting machine crystallizer liquid level data is greater than a first threshold value and less than a second threshold value, it is determined that the crystallizer of the continuous casting machine is in the second state;

[0085] When the standard deviation of the continuous casting machine casting speed data and the continuous casting machine crystallizer liquid level data is greater than a second threshold, it is determined that the crystallizer of the continuous casting machine is in the third state.

[0086] The continuous casting machine crystallizer leakage identification system of the present invention obtains crystallizer thermocouple data, and determines the bonding feature change time according to the crystallizer thermocouple data; obtains the continuous casting machine pulling speed data and the continuous casting machine crystallizer liquid level data within a set time interval before the bonding feature change time; determines the standard deviation of the continuous casting machine pulling speed data and the continuous casting machine crystallizer liquid level data within the set time interval according to the obtained continuous casting machine pulling speed data and the continuous casting machine crystallizer liquid level data; when the standard deviation of the continuous casting machine pulling speed data and the continuous casting machine crystallizer liquid level data is less than the first threshold, it is determined that the crystallizer of the continuous casting machine is in the first state; when the standard deviation of the continuous casting machine pulling speed data and the continuous casting machine crystallizer liquid level data is greater than the first threshold and less than the second threshold, it is determined that the crystallizer of the continuous casting machine is in the second state; when the standard deviation of the continuous casting machine pulling speed data and the continuous casting machine crystallizer liquid level data is greater than the second threshold, it is determined that the crystallizer of the continuous casting machine is in the third state. The present invention ultimately achieves the technical effect of greatly reducing the false alarm rate in the non-steady-state continuous casting production scenario.

[0087] For more specific implementations of the above-mentioned continuous casting machine mold leak identification system, reference can be made to the above-mentioned embodiment description of the continuous casting machine mold leak identification method, which will not be described in detail here.

[0088] like Figure 4 As shown, the present invention also provides an electronic device 1 for a method for identifying steel leakage in a continuous casting machine crystallizer.

[0089] The electronic device 1 may include a processor 10, a memory 11 and a bus, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as a continuous casting machine crystallizer breakout identification program 12. The memory 11 may also include both an internal storage unit of the continuous casting machine crystallizer breakout identification system and an external storage device. The memory 11 may be used not only to store application software and various data installed therein, such as the code of the continuous casting machine crystallizer breakout identification program, but also to temporarily store data that has been output or is to be output.

[0090] The electronic device 1 may include a processor 10, a memory 11 and a bus, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as a continuous casting machine crystallizer breakout identification program 12. The memory 11 may also include both an internal storage unit of the continuous casting machine crystallizer breakout identification system and an external storage device. The memory 11 may be used not only to store application software and various data installed therein, such as the code of the continuous casting machine crystallizer breakout identification program, but also to temporarily store data that has been output or is to be output.

[0091] Wherein, the memory 11 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, mobile hard disk, multimedia card, card-type memory (for example: SD or DX memory, etc.), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 11 can be an internal storage unit of the electronic device 1, such as a mobile hard disk of the electronic device 1. In other embodiments, the memory 11 can also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device 1. Further, the memory 11 can also include both an internal storage unit of the electronic device 1 and an external storage device. The memory 11 can not only be used to store application software and various types of data installed in the electronic device 1, such as the code of the continuous casting machine crystallizer leak identification program, but also can be used to temporarily store data that has been output or is to be output.

[0092] The processor 10 may be composed of an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and combinations of various control chips, etc. The processor 10 is the control core (Control Unit) of the electronic device, and uses various interfaces and lines to connect various components of the entire electronic device, and executes or executes programs or modules (such as continuous casting machine crystallizer leak identification programs, etc.) stored in the memory 11, and calls data stored in the memory 11 to execute various functions of the electronic device 1 and process data.

[0093] The bus may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is configured to realize connection and communication between the memory 11 and at least one processor 10, etc.

[0094] Figure 4 Only an electronic device with components is shown, and those skilled in the art will understand that Figure 4 The structure shown does not constitute a limitation on the electronic device 1, and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0095] For example, although not shown, the electronic device 1 may also include a power source (such as a battery) for supplying power to each component. Preferably, the power source may be logically connected to the at least one processor 10 through a power management system, so that the power management system can realize functions such as charging management, discharging management, and power consumption management. The power source may also include any components such as one or more DC or AC power sources, recharging systems, power failure detection circuits, power converters or inverters, and power status indicators. The electronic device 1 may also include a variety of sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

[0096] Furthermore, the electronic device 1 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device 1 and other electronic devices.

[0097] Optionally, the electronic device 1 may further include a user interface, which may be a display, an input unit (such as a keyboard), or a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode) touch device. The display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device 1 and to display a visual user interface.

[0098] It should be understood that the embodiment is for illustration only and the scope of the patent application is not limited to this structure.

[0099] The continuous casting machine crystallizer leak identification program 12 stored in the memory 11 in the electronic device 1 is a combination of multiple instructions. When running in the processor 10, it can achieve: obtaining crystallizer thermocouple data, and determining the bonding feature change time according to the crystallizer thermocouple data; obtaining the continuous casting machine pulling speed data and the continuous casting machine crystallizer liquid level data within a set time interval before the bonding feature change time; determining the continuous casting machine pulling speed data and the continuous casting machine crystallizer liquid level data within the set time interval according to the obtained continuous casting machine pulling speed data and the continuous casting machine crystallizer liquid level data. The method comprises the following steps: determining a state of the crystallizer of the continuous casting machine according to the standard deviation of the pulling speed data of the continuous casting machine and the liquid level data of the crystallizer of the continuous casting machine; determining a state of the crystallizer of the continuous casting machine according to the standard deviation; wherein, when the standard deviation of the pulling speed data of the continuous casting machine and the liquid level data of the crystallizer of the continuous casting machine is less than a first threshold value, determining that the crystallizer of the continuous casting machine is in a first state; when the standard deviation of the pulling speed data of the continuous casting machine and the liquid level data of the crystallizer of the continuous casting machine is greater than the first threshold value and less than the second threshold value, determining that the crystallizer of the continuous casting machine is in a second state; when the standard deviation of the pulling speed data of the continuous casting machine and the liquid level data of the crystallizer of the continuous casting machine is greater than the second threshold value, determining that the crystallizer of the continuous casting machine is in a third state.

[0100] Specifically, the specific implementation method of the processor 10 for the above instructions can refer to Figure 1 The description of the relevant steps in the corresponding embodiment is not repeated here. Furthermore, if the module / unit integrated in the electronic device 1 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. The computer-readable medium may include: any entity or system capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).

[0101] The embodiment of the present invention further provides a computer-readable storage medium, which may be non-volatile or volatile, and stores a computer program. When the computer program is executed by a processor, the following is achieved: obtaining crystallizer thermocouple data, and determining the bonding characteristic change moment according to the crystallizer thermocouple data; obtaining continuous casting machine pulling speed data and continuous casting machine crystallizer liquid level data within a set time interval before the bonding characteristic change moment; determining the continuous casting machine pulling speed data within a set time interval according to the obtained continuous casting machine pulling speed data and continuous casting machine crystallizer liquid level data. and the standard deviation of the continuous casting machine crystallizer liquid level data; determine the state of the continuous casting machine crystallizer according to the standard deviation; wherein, when the standard deviation of the continuous casting machine pulling speed data and the continuous casting machine crystallizer liquid level data is less than a first threshold value, it is determined that the crystallizer of the continuous casting machine is in a first state; when the standard deviation of the continuous casting machine pulling speed data and the continuous casting machine crystallizer liquid level data is greater than the first threshold value and less than the second threshold value, it is determined that the crystallizer of the continuous casting machine is in a second state; when the standard deviation of the continuous casting machine pulling speed data and the continuous casting machine crystallizer liquid level data is greater than the second threshold value, it is determined that the crystallizer of the continuous casting machine is in a third state.

[0102] Specifically, the specific implementation method when the computer program is executed by the processor can refer to the description of the relevant steps in the continuous casting machine crystallizer leak identification method in the embodiment, which will not be repeated here.

[0103] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, systems and methods can be implemented in other ways. For example, the system embodiments described above are only schematic, for example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.

[0104] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0105] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0106] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0107] Therefore, no matter from which point of view, the embodiments should be regarded as illustrative and non-restrictive, and the scope of the present invention is limited by the appended claims rather than the above description, so it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention. Any attached figure mark in the claims should not be regarded as limiting the claims involved.

[0108] In addition, it is obvious that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. The multiple units or systems stated in the system claim can also be implemented by one unit or system through software or hardware.

[0109] However, those skilled in the art should understand that various improvements can be made to the continuous casting machine mold steel leakage identification method and continuous casting machine mold steel leakage identification system proposed in the present invention without departing from the content of the present invention. Therefore, the protection scope of the present invention should be determined by the content of the attached claims.

Claims

1. A method for identifying steel leakage in a continuous casting machine crystallizer, applied to electronic equipment, characterized in that: include: Acquiring mold thermocouple data, and determining a bonding characteristic change time according to the mold thermocouple data; Acquire the continuous casting machine casting speed data and the continuous casting machine crystallizer liquid level data within a set time interval before the bonding characteristic changes; Determine the standard deviation of the continuous casting machine casting speed data and the continuous casting machine crystallizer liquid level data within a set time interval according to the acquired continuous casting machine casting speed data and the continuous casting machine crystallizer liquid level data; The state of the crystallizer of the continuous casting machine is determined according to the standard deviation; wherein, when the standard deviation of the continuous casting machine pulling speed data and the continuous casting machine crystallizer liquid level data is less than a first threshold value, the crystallizer of the continuous casting machine is determined to be in a first state; when the standard deviation of the continuous casting machine pulling speed data and the continuous casting machine crystallizer liquid level data is greater than the first threshold value and less than the second threshold value, the crystallizer of the continuous casting machine is determined to be in a second state; when the standard deviation of the continuous casting machine pulling speed data and the continuous casting machine crystallizer liquid level data is greater than the second threshold value, the crystallizer of the continuous casting machine is determined to be in a third state.

2. The continuous casting machine mold breakout identification method according to claim 1, characterized in that: The first state is a stable operation state of the continuous casting machine; The second state is a second stable operation state of the continuous casting machine; The third state is an abnormal operation state of the continuous casting machine.

3. The continuous casting machine mold breakout identification method according to claim 2, characterized in that: When it is determined that the crystallizer of the continuous casting machine is in the first state, the initial temperature and the maximum temperature of the upper thermocouple when the temperature rise curve appears, the initial temperature and the maximum temperature of the lower thermocouple when the temperature rise curve appears, the high temperature point moment of the upper thermocouple, the high temperature point moment of the lower thermocouple, the actual physical distance between the upper thermocouple and the lower thermocouple, and the continuous casting machine casting speed data are obtained; The minimum temperature rise of the upper thermocouple is obtained according to the initial temperature and the maximum temperature of the upper thermocouple when the temperature rise curve appears, and the minimum temperature rise of the lower thermocouple is obtained according to the initial temperature and the maximum temperature of the lower thermocouple when the temperature rise curve appears; the speed of the high temperature point moving between the two thermocouples at the moment of the high temperature point is obtained according to the high temperature point moment of the upper thermocouple, the high temperature point moment of the lower thermocouple, and the actual physical distance between the upper thermocouple and the lower thermocouple; and then the ratio of the speed of the high temperature point moving between the two thermocouples at the moment of the high temperature point to the casting speed is determined according to the speed of the high temperature point moving between the two thermocouples at the moment of the high temperature point and the casting speed data of the continuous casting machine; The crystallizer state is monitored according to the first rule; if the minimum temperature rise of the upper thermocouple or the minimum temperature rise of the lower thermocouple and the ratio of the speed of the high temperature point moving between the two thermocouples to the pulling speed meet the first rule, a steel leakage alarm signal is issued.

4. The continuous casting machine mold breakout identification method according to claim 2, characterized in that: When it is determined that the crystallizer of the continuous casting machine is in the second state, the initial temperature and the maximum temperature of the upper thermocouple when the temperature rise curve appears, the initial temperature and the maximum temperature of the lower thermocouple when the temperature rise curve appears, the high temperature point moment of the upper thermocouple, the high temperature point moment of the lower thermocouple, the actual physical distance between the upper thermocouple and the lower thermocouple, and the continuous casting machine casting speed data are obtained; The minimum temperature rise of the upper thermocouple is obtained according to the initial temperature and the maximum temperature of the upper thermocouple when the temperature rise curve appears, and the minimum temperature rise of the lower thermocouple is obtained according to the initial temperature and the maximum temperature of the lower thermocouple when the temperature rise curve appears; the speed of the high temperature point moving between the two thermocouples at the moment of the high temperature point is obtained according to the high temperature point moment of the upper thermocouple, the high temperature point moment of the lower thermocouple, and the actual physical distance between the upper thermocouple and the lower thermocouple; and then the ratio of the speed of the high temperature point moving between the two thermocouples at the moment of the high temperature point to the casting speed is determined according to the speed of the high temperature point moving between the two thermocouples at the moment of the high temperature point and the casting speed data of the continuous casting machine; The crystallizer state is monitored according to the second rule; if the minimum temperature rise of the upper thermocouple or the minimum temperature rise of the lower thermocouple and the ratio of the speed of the high temperature point moving between the two thermocouples to the pulling speed meet the second rule, a steel leakage alarm signal is issued.

5. The continuous casting machine mold breakout identification method according to claim 2, characterized in that: When it is determined that the crystallizer of the continuous casting machine is in the third state, the continuous casting machine casting speed data, the continuous casting machine crystallizer liquid level data and the crystallizer vibration data are obtained; The abnormal state type of the continuous casting machine is determined according to the acquired continuous casting machine casting speed data, continuous casting machine crystallizer liquid level data and crystallizer vibration data.

6. The continuous casting machine mold breakout identification method according to claim 5, characterized in that: The abnormal state types of the continuous casting machine include any one of the casting machine water inlet replacement state, tundish replacement state, abnormal accident state, casting start and casting speed increase state, casting stop state, casting speed increase state and casting speed reduction state.

7. The continuous casting machine mold breakout identification method according to claim 5, characterized in that: After determining the abnormal state type of the continuous casting machine according to the acquired continuous casting machine casting speed data, continuous casting machine crystallizer liquid level data and crystallizer vibration data, the method further includes: According to the abnormal state type of the continuous casting machine, steel leakage monitoring is performed according to the determination rules corresponding to the abnormal state type of the continuous casting machine.

8. A continuous casting machine mold leak identification system, which uses the continuous casting machine mold leak identification method according to any one of claims 1 to 7 to identify leaks; the system comprises: A data acquisition unit, used to acquire mold thermocouple data and determine the bonding characteristic change time according to the mold thermocouple data; Acquire the continuous casting machine casting speed data and the continuous casting machine crystallizer liquid level data within a set time interval before the bonding characteristic changes; A data processing unit is used to determine the standard deviation of the continuous casting machine casting speed data and the continuous casting machine crystallizer liquid level data within a set time interval according to the acquired continuous casting machine casting speed data and the continuous casting machine crystallizer liquid level data; A state determination unit is used to determine the state of the crystallizer of the continuous casting machine according to the standard deviation; wherein, when the standard deviation of the continuous casting machine pulling speed data and the continuous casting machine crystallizer liquid level data is less than a first threshold value, the crystallizer of the continuous casting machine is determined to be in a first state; when the standard deviation of the continuous casting machine pulling speed data and the continuous casting machine crystallizer liquid level data is greater than the first threshold value and less than a second threshold value, the crystallizer of the continuous casting machine is determined to be in a second state; when the standard deviation of the continuous casting machine pulling speed data and the continuous casting machine crystallizer liquid level data is greater than the second threshold value, the crystallizer of the continuous casting machine is determined to be in a third state.

9. An electronic device, characterized in that: The electronic device includes a memory, a processor, and a continuous casting machine crystallizer steel leakage identification program stored in the memory and executable on the processor. When the continuous casting machine crystallizer steel leakage identification program is executed by the processor, the steps of the continuous casting machine crystallizer steel leakage identification method as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for identifying steel leakage in a continuous casting machine crystallizer as claimed in any one of claims 1 to 7 is implemented.

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